Overview

Dataset statistics

Number of variables15
Number of observations1158
Missing cells9
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory198.0 KiB
Average record size in memory175.1 B

Variable types

NUM12
CAT3

Warnings

D3 has a high cardinality: 1101 distinct values High cardinality
D3 is uniformly distributed Uniform
PanelistIdQuestion has unique values Unique
B5a_0 has 55 (4.7%) zeros Zeros
B5a_1 has 66 (5.7%) zeros Zeros
B5a_2 has 67 (5.8%) zeros Zeros
B5a_3 has 177 (15.3%) zeros Zeros
B5a_4 has 93 (8.0%) zeros Zeros
B5a_5 has 136 (11.7%) zeros Zeros
B5a_6 has 187 (16.1%) zeros Zeros
B5a_7 has 127 (11.0%) zeros Zeros
B5a_8 has 86 (7.4%) zeros Zeros

Reproduction

Analysis started2020-12-12 20:13:32.292446
Analysis finished2020-12-12 20:13:43.462558
Duration11.17 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

PanelistIdQuestion
Real number (ℝ≥0)

UNIQUE

Distinct1158
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3801895.842
Minimum3694164
Maximum3932495
Zeros0
Zeros (%)0.0%
Memory size9.2 KiB
2020-12-12T15:13:43.524112image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum3694164
5-th percentile3696063.85
Q13700357.25
median3742668.5
Q33918523.75
95-th percentile3925388.35
Maximum3932495
Range238331
Interquartile range (IQR)218166.5

Descriptive statistics

Standard deviation102201.1899
Coefficient of variation (CV)0.02688163856
Kurtosis-1.86532239
Mean3801895.842
Median Absolute Deviation (MAD)47190.5
Skewness0.1673013397
Sum4402595385
Variance1.044508321e+10
MonotocityNot monotonic
2020-12-12T15:13:43.605682image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
371097310.1%
 
376293310.1%
 
370138310.1%
 
392666410.1%
 
391985210.1%
 
370138610.1%
 
370958110.1%
 
370139010.1%
 
371170910.1%
 
370187410.1%
 
392872210.1%
 
370958710.1%
 
373007210.1%
 
391740610.1%
 
393077810.1%
 
392053910.1%
 
370959610.1%
 
392051710.1%
 
373005210.1%
 
370137910.1%
 
370136510.1%
 
369521210.1%
 
369521310.1%
 
369521410.1%
 
392049510.1%
 
Other values (1133)113397.8%
 
ValueCountFrequency (%) 
369416410.1%
 
369456010.1%
 
369501610.1%
 
369502510.1%
 
369503210.1%
 
369503510.1%
 
369504010.1%
 
369504410.1%
 
369507310.1%
 
369507710.1%
 
ValueCountFrequency (%) 
393249510.1%
 
393212110.1%
 
393207310.1%
 
393177710.1%
 
393177610.1%
 
393171310.1%
 
393156510.1%
 
393139210.1%
 
393130110.1%
 
393113910.1%
 

D1
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.2 KiB
1
711 
2
447 
ValueCountFrequency (%) 
171161.4%
 
244738.6%
 
2020-12-12T15:13:43.677744image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T15:13:43.717778image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:43.760315image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters2
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
171161.4%
 
244738.6%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number1158100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
171161.4%
 
244738.6%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1158100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
171161.4%
 
244738.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1158100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
171161.4%
 
244738.6%
 

D2
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.2 KiB
2
446 
4
300 
3
289 
5
123 
ValueCountFrequency (%) 
244638.5%
 
430025.9%
 
328925.0%
 
512310.6%
 
2020-12-12T15:13:43.824871image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T15:13:43.869910image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:43.917951image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
244638.5%
 
430025.9%
 
328925.0%
 
512310.6%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number1158100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
244638.5%
 
430025.9%
 
328925.0%
 
512310.6%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1158100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
244638.5%
 
430025.9%
 
328925.0%
 
512310.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1158100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
244638.5%
 
430025.9%
 
328925.0%
 
512310.6%
 

D3
Categorical

HIGH CARDINALITY
UNIFORM

Distinct1101
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Memory size9.2 KiB
T5Y4M2
 
4
T5K2Y3
 
3
T5J0B5
 
3
T5H1E8
 
3
T5J1R9
 
3
Other values (1096)
1142 
ValueCountFrequency (%) 
T5Y4M240.3%
 
T5K2Y330.3%
 
T5J0B530.3%
 
T5H1E830.3%
 
T5J1R930.3%
 
T5K2L130.3%
 
T6J0R520.2%
 
T6R2W720.2%
 
T5J1A520.2%
 
T6L6X820.2%
 
T6J3W620.2%
 
T5J2R720.2%
 
T5K1M120.2%
 
t5j1a720.2%
 
T6H2W120.2%
 
T5N1N420.2%
 
T5T3X420.2%
 
T6C1E220.2%
 
t5a2c820.2%
 
T6E4J520.2%
 
T5Y0E620.2%
 
T6E6P520.2%
 
T5T2C620.2%
 
T5W0K120.2%
 
T6E2C320.2%
 
Other values (1076)110195.1%
 
2020-12-12T15:13:43.995017image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1051 ?
Unique (%)90.8%
2020-12-12T15:13:44.066579image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length6
Mean length6
Min length6

Overview of Unicode Properties

Unique unicode characters50
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
T93813.5%
 
675910.9%
 
575710.9%
 
23875.6%
 
13855.5%
 
t3445.0%
 
32974.3%
 
02834.1%
 
42383.4%
 
71372.0%
 
R1341.9%
 
E1311.9%
 
81251.8%
 
H1111.6%
 
K1091.6%
 
91061.5%
 
J1041.5%
 
L901.3%
 
C871.3%
 
M861.2%
 
W811.2%
 
G811.2%
 
A761.1%
 
B721.0%
 
N630.9%
 
Other values (25)96713.9%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number347450.0%
 
Uppercase Letter245735.4%
 
Lowercase Letter101714.6%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
t34433.8%
 
h565.5%
 
r525.1%
 
j515.0%
 
c504.9%
 
a494.8%
 
k464.5%
 
e424.1%
 
b403.9%
 
m353.4%
 
g333.2%
 
l323.1%
 
w313.0%
 
p272.7%
 
x272.7%
 
y252.5%
 
z252.5%
 
n181.8%
 
s171.7%
 
v171.7%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
675921.8%
 
575721.8%
 
238711.1%
 
138511.1%
 
32978.5%
 
02838.1%
 
42386.9%
 
71373.9%
 
81253.6%
 
91063.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
T93838.2%
 
R1345.5%
 
E1315.3%
 
H1114.5%
 
K1094.4%
 
J1044.2%
 
L903.7%
 
C873.5%
 
M863.5%
 
W813.3%
 
G813.3%
 
A763.1%
 
B722.9%
 
N632.6%
 
X632.6%
 
Y582.4%
 
P492.0%
 
V471.9%
 
S411.7%
 
Z361.5%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin347450.0%
 
Common347450.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
T93827.0%
 
t3449.9%
 
R1343.9%
 
E1313.8%
 
H1113.2%
 
K1093.1%
 
J1043.0%
 
L902.6%
 
C872.5%
 
M862.5%
 
W812.3%
 
G812.3%
 
A762.2%
 
B722.1%
 
N631.8%
 
X631.8%
 
Y581.7%
 
h561.6%
 
r521.5%
 
j511.5%
 
c501.4%
 
a491.4%
 
P491.4%
 
V471.4%
 
k461.3%
 
Other values (15)44612.8%
 

Most frequent Common characters

ValueCountFrequency (%) 
675921.8%
 
575721.8%
 
238711.1%
 
138511.1%
 
32978.5%
 
02838.1%
 
42386.9%
 
71373.9%
 
81253.6%
 
91063.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII6948100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
T93813.5%
 
675910.9%
 
575710.9%
 
23875.6%
 
13855.5%
 
t3445.0%
 
32974.3%
 
02834.1%
 
42383.4%
 
71372.0%
 
R1341.9%
 
E1311.9%
 
81251.8%
 
H1111.6%
 
K1091.6%
 
91061.5%
 
J1041.5%
 
L901.3%
 
C871.3%
 
M861.2%
 
W811.2%
 
G811.2%
 
A761.1%
 
B721.0%
 
N630.9%
 
Other values (25)96713.9%
 

CIVIC_WARD
Real number (ℝ≥0)

Distinct13
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.82642487
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Memory size9.2 KiB
2020-12-12T15:13:44.123127image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median7
Q39
95-th percentile12
Maximum99
Range98
Interquartile range (IQR)4

Descriptive statistics

Standard deviation10.19397484
Coefficient of variation (CV)1.302507212
Kurtosis69.28907159
Mean7.82642487
Median Absolute Deviation (MAD)2
Skewness8.018501146
Sum9063
Variance103.9171231
MonotocityNot monotonic
2020-12-12T15:13:44.183179image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%) 
618115.6%
 
815813.6%
 
912610.9%
 
51018.7%
 
10958.2%
 
11857.3%
 
4746.4%
 
2736.3%
 
1716.1%
 
12665.7%
 
7635.4%
 
3524.5%
 
99131.1%
 
ValueCountFrequency (%) 
1716.1%
 
2736.3%
 
3524.5%
 
4746.4%
 
51018.7%
 
618115.6%
 
7635.4%
 
815813.6%
 
912610.9%
 
10958.2%
 
ValueCountFrequency (%) 
99131.1%
 
12665.7%
 
11857.3%
 
10958.2%
 
912610.9%
 
815813.6%
 
7635.4%
 
618115.6%
 
51018.7%
 
4746.4%
 

B11
Real number (ℝ≥0)

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.914507772
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Memory size9.2 KiB
2020-12-12T15:13:44.239227image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.448193901
Coefficient of variation (CV)0.3699555564
Kurtosis-0.9820473473
Mean3.914507772
Median Absolute Deviation (MAD)1
Skewness-0.3739785636
Sum4533
Variance2.097265574
MonotocityNot monotonic
2020-12-12T15:13:44.297778image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
538833.5%
 
419717.0%
 
219216.6%
 
318516.0%
 
613411.6%
 
1625.4%
 
ValueCountFrequency (%) 
1625.4%
 
219216.6%
 
318516.0%
 
419717.0%
 
538833.5%
 
613411.6%
 
ValueCountFrequency (%) 
613411.6%
 
538833.5%
 
419717.0%
 
318516.0%
 
219216.6%
 
1625.4%
 

B5a_0
Real number (ℝ≥0)

ZEROS

Distinct45
Distinct (%)3.9%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean12.25410545
Minimum0
Maximum100
Zeros55
Zeros (%)4.7%
Memory size9.2 KiB
2020-12-12T15:13:44.367838image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q18
median10
Q315
95-th percentile25
Maximum100
Range100
Interquartile range (IQR)7

Descriptive statistics

Standard deviation7.84347816
Coefficient of variation (CV)0.6400694196
Kurtosis21.21384334
Mean12.25410545
Median Absolute Deviation (MAD)3
Skewness3.012346446
Sum14178
Variance61.52014965
MonotocityNot monotonic
2020-12-12T15:13:44.446405image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%) 
1028524.6%
 
1511810.2%
 
5726.2%
 
12685.9%
 
8665.7%
 
20585.0%
 
0554.7%
 
9504.3%
 
7504.3%
 
11443.8%
 
13423.6%
 
6292.5%
 
14292.5%
 
25282.4%
 
19201.7%
 
18181.6%
 
16161.4%
 
17151.3%
 
4141.2%
 
30100.9%
 
2290.8%
 
2670.6%
 
2460.5%
 
2350.4%
 
2140.3%
 
Other values (20)393.4%
 
ValueCountFrequency (%) 
0554.7%
 
110.1%
 
240.3%
 
330.3%
 
4141.2%
 
5726.2%
 
6292.5%
 
7504.3%
 
8665.7%
 
9504.3%
 
ValueCountFrequency (%) 
10010.1%
 
7510.1%
 
5910.1%
 
5810.1%
 
5420.2%
 
5020.2%
 
4710.1%
 
4610.1%
 
4030.3%
 
3530.3%
 

B5a_1
Real number (ℝ≥0)

ZEROS

Distinct59
Distinct (%)5.1%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean20.41832325
Minimum0
Maximum81
Zeros66
Zeros (%)5.7%
Memory size9.2 KiB
2020-12-12T15:13:44.526474image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114
median20
Q325
95-th percentile40
Maximum81
Range81
Interquartile range (IQR)11

Descriptive statistics

Standard deviation11.4935491
Coefficient of variation (CV)0.5629036703
Kurtosis1.579766011
Mean20.41832325
Median Absolute Deviation (MAD)6
Skewness0.6855737126
Sum23624
Variance132.1016709
MonotocityNot monotonic
2020-12-12T15:13:44.608545image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2017415.0%
 
25887.6%
 
15847.3%
 
30696.0%
 
0665.7%
 
10635.4%
 
22383.3%
 
18363.1%
 
17312.7%
 
24282.4%
 
19282.4%
 
35272.3%
 
16262.2%
 
5262.2%
 
21221.9%
 
40211.8%
 
12191.6%
 
11191.6%
 
8191.6%
 
50181.6%
 
14181.6%
 
23171.5%
 
33171.5%
 
27131.1%
 
31131.1%
 
Other values (34)17715.3%
 
ValueCountFrequency (%) 
0665.7%
 
150.4%
 
240.3%
 
3110.9%
 
460.5%
 
5262.2%
 
6110.9%
 
770.6%
 
8191.6%
 
9131.1%
 
ValueCountFrequency (%) 
8110.1%
 
8010.1%
 
6610.1%
 
6210.1%
 
5910.1%
 
5530.3%
 
5310.1%
 
5110.1%
 
50181.6%
 
4940.3%
 

B5a_2
Real number (ℝ≥0)

ZEROS

Distinct36
Distinct (%)3.1%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean10.90924806
Minimum0
Maximum68
Zeros67
Zeros (%)5.8%
Memory size9.2 KiB
2020-12-12T15:13:44.689115image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median10
Q315
95-th percentile22
Maximum68
Range68
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.965154934
Coefficient of variation (CV)0.6384633385
Kurtosis3.702624273
Mean10.90924806
Median Absolute Deviation (MAD)5
Skewness0.962598077
Sum12622
Variance48.51338326
MonotocityNot monotonic
2020-12-12T15:13:44.757173image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%) 
1019616.9%
 
512911.1%
 
151049.0%
 
20958.2%
 
0675.8%
 
8534.6%
 
7504.3%
 
6413.5%
 
12403.5%
 
2363.1%
 
4322.8%
 
9302.6%
 
16292.5%
 
3282.4%
 
18272.3%
 
17272.3%
 
11262.2%
 
13232.0%
 
14161.4%
 
22151.3%
 
19151.3%
 
1151.3%
 
25141.2%
 
23110.9%
 
21100.9%
 
Other values (11)282.4%
 
ValueCountFrequency (%) 
0675.8%
 
1151.3%
 
2363.1%
 
3282.4%
 
4322.8%
 
512911.1%
 
6413.5%
 
7504.3%
 
8534.6%
 
9302.6%
 
ValueCountFrequency (%) 
6810.1%
 
4010.1%
 
3910.1%
 
3520.2%
 
3110.1%
 
3060.5%
 
2920.2%
 
2820.2%
 
2720.2%
 
2630.3%
 

B5a_3
Real number (ℝ≥0)

ZEROS

Distinct37
Distinct (%)3.2%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean5.458945549
Minimum0
Maximum50
Zeros177
Zeros (%)15.3%
Memory size9.2 KiB
2020-12-12T15:13:44.827734image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q37
95-th percentile15
Maximum50
Range50
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.830454877
Coefficient of variation (CV)1.068055145
Kurtosis10.25270387
Mean5.458945549
Median Absolute Deviation (MAD)2
Skewness2.555221358
Sum6316
Variance33.99420408
MonotocityNot monotonic
2020-12-12T15:13:44.903299image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%) 
219717.0%
 
017715.3%
 
515913.7%
 
312210.5%
 
4907.8%
 
10776.6%
 
6524.5%
 
8484.1%
 
7433.7%
 
1363.1%
 
15292.5%
 
9191.6%
 
13151.3%
 
14141.2%
 
11141.2%
 
12110.9%
 
2070.6%
 
1850.4%
 
2550.4%
 
1740.3%
 
2340.3%
 
2430.3%
 
1630.3%
 
1930.3%
 
2620.2%
 
Other values (12)181.6%
 
ValueCountFrequency (%) 
017715.3%
 
1363.1%
 
219717.0%
 
312210.5%
 
4907.8%
 
515913.7%
 
6524.5%
 
7433.7%
 
8484.1%
 
9191.6%
 
ValueCountFrequency (%) 
5010.1%
 
4810.1%
 
4110.1%
 
3620.2%
 
3510.1%
 
3410.1%
 
3110.1%
 
3020.2%
 
2920.2%
 
2820.2%
 

B5a_4
Real number (ℝ≥0)

ZEROS

Distinct55
Distinct (%)4.8%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean20.57562662
Minimum0
Maximum100
Zeros93
Zeros (%)8.0%
Memory size9.2 KiB
2020-12-12T15:13:44.981366image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q115
median21
Q327
95-th percentile38
Maximum100
Range100
Interquartile range (IQR)12

Descriptive statistics

Standard deviation11.03904022
Coefficient of variation (CV)0.5365105238
Kurtosis2.342059671
Mean20.57562662
Median Absolute Deviation (MAD)6
Skewness0.2734603327
Sum23806
Variance121.8604089
MonotocityNot monotonic
2020-12-12T15:13:45.063437image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2014312.3%
 
251089.3%
 
301079.2%
 
0938.0%
 
22655.6%
 
15615.3%
 
21564.8%
 
10363.1%
 
24312.7%
 
23312.7%
 
35292.5%
 
5282.4%
 
40252.2%
 
12232.0%
 
27221.9%
 
26211.8%
 
19211.8%
 
18181.6%
 
33171.5%
 
28171.5%
 
17151.3%
 
4141.2%
 
16131.1%
 
29121.0%
 
8121.0%
 
Other values (30)13912.0%
 
ValueCountFrequency (%) 
0938.0%
 
160.5%
 
240.3%
 
360.5%
 
4141.2%
 
5282.4%
 
680.7%
 
780.7%
 
8121.0%
 
990.8%
 
ValueCountFrequency (%) 
10010.1%
 
6710.1%
 
6110.1%
 
5710.1%
 
5510.1%
 
5410.1%
 
5310.1%
 
5040.3%
 
4910.1%
 
4610.1%
 

B5a_5
Real number (ℝ≥0)

ZEROS

Distinct35
Distinct (%)3.0%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean6.448573898
Minimum0
Maximum70
Zeros136
Zeros (%)11.7%
Memory size9.2 KiB
2020-12-12T15:13:45.139502image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q310
95-th percentile17
Maximum70
Range70
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.973861038
Coefficient of variation (CV)0.9263848305
Kurtosis14.7420681
Mean6.448573898
Median Absolute Deviation (MAD)3
Skewness2.485651359
Sum7461
Variance35.6870157
MonotocityNot monotonic
2020-12-12T15:13:45.208061image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%) 
519416.8%
 
013611.7%
 
1013611.7%
 
21038.9%
 
1817.0%
 
3746.4%
 
4675.8%
 
6645.5%
 
8554.7%
 
7373.2%
 
15332.8%
 
9312.7%
 
12272.3%
 
11232.0%
 
20201.7%
 
13141.2%
 
16121.0%
 
1490.8%
 
1780.7%
 
2560.5%
 
2150.4%
 
2440.3%
 
2240.3%
 
1920.2%
 
1820.2%
 
Other values (10)100.9%
 
ValueCountFrequency (%) 
013611.7%
 
1817.0%
 
21038.9%
 
3746.4%
 
4675.8%
 
519416.8%
 
6645.5%
 
7373.2%
 
8554.7%
 
9312.7%
 
ValueCountFrequency (%) 
7010.1%
 
4410.1%
 
4010.1%
 
3810.1%
 
3710.1%
 
3510.1%
 
3010.1%
 
2810.1%
 
2610.1%
 
2560.5%
 

B5a_6
Real number (ℝ≥0)

ZEROS

Distinct30
Distinct (%)2.6%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean5.762316335
Minimum0
Maximum38
Zeros187
Zeros (%)16.1%
Memory size9.2 KiB
2020-12-12T15:13:45.279623image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile16
Maximum38
Range38
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.275919457
Coefficient of variation (CV)0.9155900423
Kurtosis4.017081602
Mean5.762316335
Median Absolute Deviation (MAD)3
Skewness1.599927859
Sum6667
Variance27.83532612
MonotocityNot monotonic
2020-12-12T15:13:45.346180image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%) 
519717.0%
 
018716.1%
 
101028.8%
 
3958.2%
 
2907.8%
 
4786.7%
 
6716.1%
 
1615.3%
 
7534.6%
 
8474.1%
 
15322.8%
 
9292.5%
 
12201.7%
 
11201.7%
 
20181.6%
 
13100.9%
 
1790.8%
 
1880.7%
 
1450.4%
 
2250.4%
 
1640.3%
 
2130.3%
 
2520.2%
 
2420.2%
 
3820.2%
 
Other values (5)70.6%
 
ValueCountFrequency (%) 
018716.1%
 
1615.3%
 
2907.8%
 
3958.2%
 
4786.7%
 
519717.0%
 
6716.1%
 
7534.6%
 
8474.1%
 
9292.5%
 
ValueCountFrequency (%) 
3820.2%
 
3010.1%
 
2820.2%
 
2720.2%
 
2520.2%
 
2420.2%
 
2310.1%
 
2250.4%
 
2130.3%
 
20181.6%
 

B5a_7
Real number (ℝ≥0)

ZEROS

Distinct18
Distinct (%)1.6%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean3.504753673
Minimum0
Maximum20
Zeros127
Zeros (%)11.0%
Memory size9.2 KiB
2020-12-12T15:13:45.414238image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q35
95-th percentile9
Maximum20
Range20
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.675536982
Coefficient of variation (CV)0.7634022907
Kurtosis2.889769628
Mean3.504753673
Median Absolute Deviation (MAD)2
Skewness1.226489792
Sum4055
Variance7.158498144
MonotocityNot monotonic
2020-12-12T15:13:45.477293image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%) 
522319.3%
 
221118.2%
 
315213.1%
 
114812.8%
 
012711.0%
 
411910.3%
 
6524.5%
 
7393.4%
 
10383.3%
 
8262.2%
 
980.7%
 
1530.3%
 
1330.3%
 
1130.3%
 
1220.2%
 
2010.1%
 
1410.1%
 
1810.1%
 
(Missing)10.1%
 
ValueCountFrequency (%) 
012711.0%
 
114812.8%
 
221118.2%
 
315213.1%
 
411910.3%
 
522319.3%
 
6524.5%
 
7393.4%
 
8262.2%
 
980.7%
 
ValueCountFrequency (%) 
2010.1%
 
1810.1%
 
1530.3%
 
1410.1%
 
1330.3%
 
1220.2%
 
1130.3%
 
10383.3%
 
980.7%
 
8262.2%
 

B5a_8
Real number (ℝ≥0)

ZEROS

Distinct33
Distinct (%)2.9%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean8.439930856
Minimum0
Maximum75
Zeros86
Zeros (%)7.4%
Memory size9.2 KiB
2020-12-12T15:13:45.546352image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median8
Q312
95-th percentile18
Maximum75
Range75
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.945330173
Coefficient of variation (CV)0.7044287772
Kurtosis16.3598161
Mean8.439930856
Median Absolute Deviation (MAD)3
Skewness2.132706684
Sum9765
Variance35.34695086
MonotocityNot monotonic
2020-12-12T15:13:45.616412image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%) 
1016714.4%
 
513811.9%
 
0867.4%
 
15716.1%
 
7675.8%
 
3625.4%
 
6615.3%
 
4605.2%
 
8595.1%
 
12524.5%
 
13504.3%
 
9484.1%
 
2453.9%
 
11413.5%
 
1292.5%
 
20262.2%
 
14242.1%
 
16221.9%
 
17110.9%
 
18100.9%
 
2170.6%
 
2550.4%
 
1940.3%
 
2420.2%
 
2620.2%
 
Other values (8)80.7%
 
ValueCountFrequency (%) 
0867.4%
 
1292.5%
 
2453.9%
 
3625.4%
 
4605.2%
 
513811.9%
 
6615.3%
 
7675.8%
 
8595.1%
 
9484.1%
 
ValueCountFrequency (%) 
7510.1%
 
5110.1%
 
4310.1%
 
3610.1%
 
3010.1%
 
2810.1%
 
2620.2%
 
2550.4%
 
2420.2%
 
2310.1%
 

Interactions

2020-12-12T15:13:32.795379image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:32.864439image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:32.933999image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:33.001056image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:33.069115image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:33.141677image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:33.212238image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:33.279796image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:33.347354image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:33.416914image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:33.486474image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:33.558036image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:33.629097image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:33.696155image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:33.762712image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:33.827768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:33.893825image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:33.963885image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:34.032444image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:34.099001image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:34.164557image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:34.233117image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:34.301676image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:34.373237image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:34.441796image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:34.507853image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:34.573409image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:34.637465image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:34.702521image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:34.772080image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:34.840640image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:34.906697image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:34.972253image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:35.040812image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:35.109871image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:35.180432image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:35.248490image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:35.315548image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:35.381605image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:35.446160image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:35.510216image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:35.579275image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:35.646833image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:35.712389image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:35.778947image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:35.848006image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:35.916565image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:35.986625image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:36.055184image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:36.129749image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:36.202812image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:36.273372image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:36.345935image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:36.422000image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:36.497065image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:36.569127image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:36.640688image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:36.715753image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:36.792319image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:36.870386image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:36.944950image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:37.016512image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:37.087573image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:37.156632image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:37.229695image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:37.303759image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:37.377823image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:37.446882image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:37.515942image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:37.587503image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:37.659565image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:37.734129image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:37.806191image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:37.873249image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:37.939306image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:38.003861image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:38.069918image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:38.139978image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:38.208538image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:38.274594image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:38.340651image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:38.410211image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:38.478270image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:38.548330image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:38.616889image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:38.682946image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:38.748002image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:38.812557image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:38.878614image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:38.948675image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:39.016733image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:39.082790image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:39.149347image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:39.218407image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:39.288467image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:39.360529image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:39.429088image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:39.499649image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:39.570209image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:39.638768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:39.708829image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:39.783893image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:39.855955image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:39.926015image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:39.995075image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:40.067137image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:40.139699image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:40.214764image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:40.288327image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:40.360890image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:40.432952image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:40.501511image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:40.571071image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:40.644634image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:40.717196image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:40.787256image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:40.858318image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:40.931381image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:41.003443image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:41.077507image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:41.151070image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:41.225634image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:41.299697image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:41.373761image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:41.448826image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:41.525392image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:41.600456image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:41.673019image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:41.745081image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:41.819645image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:41.895710image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:41.972777image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:42.047842image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:42.118402image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:42.188963image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:42.258523image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:42.329584image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:42.405149image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:42.478712image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:42.548272image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:42.617332image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:42.689394image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:42.762456image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:42.837521image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2020-12-12T15:13:45.692978image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-12-12T15:13:45.810079image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-12-12T15:13:45.928181image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-12-12T15:13:46.046282image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-12-12T15:13:46.144867image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-12-12T15:13:42.990653image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:43.156296image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:43.271394image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:13:43.376485image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Sample

First rows

PanelistIdQuestionD1D2D3CIVIC_WARDB11B5a_0B5a_1B5a_2B5a_3B5a_4B5a_5B5a_6B5a_7B5a_8
0391614122t5a2s34234.08.010.00.00.015.00.08.025.0
1391805722T5W0K175NaNNaNNaNNaNNaNNaNNaNNaNNaN
2371072623T5N2X7650.00.00.00.00.00.00.00.00.0
3371122522T5T1T2550.00.00.00.00.00.00.00.00.0
4373291622t6r0n6940.00.00.00.00.00.00.00.00.0
5373465825T5C0E1340.00.00.00.00.00.00.00.00.0
6374500823t6h3c61060.00.00.00.00.00.00.00.00.0
7385925912t6j5e61050.00.00.00.00.00.00.00.00.0
8386052114T6H4R11050.00.00.00.00.00.00.00.00.0
9392238614t6e0w21160.00.00.00.00.00.00.00.00.0

Last rows

PanelistIdQuestionD1D2D3CIVIC_WARDB11B5a_0B5a_1B5a_2B5a_3B5a_4B5a_5B5a_6B5a_7B5a_8
1148393113924T5N2B46512.025.020.03.020.010.03.02.05.0
1149393130114T6t1h212315.010.014.011.035.01.03.01.010.0
1150393139212T5J1P76413.026.011.013.015.01.04.02.015.0
1151393156515T6K0N81156.010.015.04.035.05.03.02.020.0
1152393171312t5k0y96215.015.05.00.025.08.08.08.016.0
1153393177613T5r0e51510.010.010.010.040.05.05.05.05.0
1154393177722T5K0y96213.036.010.00.06.015.00.010.010.0
1155393207322T5G1W37315.035.010.00.020.02.02.01.015.0
1156393212114T5H0T6628.09.07.00.030.037.05.01.03.0
1157393249513T6X0S312424.038.05.015.06.02.03.01.05.0